ADGS: Anomaly Detection and Localization Based on Graph Similarity in Container-Based Clouds

Lu, Chengzhi; Ye, Kejiang; Chen, Wenyan; Xu, Cheng‐Zhong

doi:10.1109/icpads47876.2019.00016

Cited by 2 publications

(2 citation statements)

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“…𝜙(x, y)t cur (y)w s (x, y)w m (x, y)w d (x, y), (8) where t new (x) is the edge tangent vector perpendicular to the image gradient g(x) = ∇I(x). And Ω(x) represents the field of x, which is a square field with a radius of r that satisfies the radial distribution, and k is the normalized value of the vector.…”

Section: Line Portrait Generation Of Face Contourmentioning

confidence: 99%

“…Some researchers have used various methods to try to solve the problem of head pose estimation. For example, adding the three-dimensional information to the image to simplify the difficulty of research, [4][5][6][7][8][9] calculation based on face landmark detection, [10][11][12] use support vector machine to classify images, use convolutional neural network (CNN) for feature extraction, [13][14][15][16] use line portraits to highlight important information and feature aggregation methods, 17,18 multi-level classification to regression, 19 and so forth.…”

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confidence: 99%

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Multi‐angle head pose classification with masks based on color texture analysis and stack generalization

Dong

Shi

et al. 2021

Concurrency and Computation

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Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H-channel image of the HSV color space is extracted through the conversion of the color space.Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image.Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

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Section: Line Portrait Generation Of Face Contourmentioning

confidence: 99%